CN111898096B - System and method for visualization of cross-border transfer analysis of football players - Google Patents

Abstract

The invention relates to a system and a method for analyzing and visualizing a cross-country transfer of football players, belonging to the technical field of data analysis and visual analysis. Comprising the following steps: a national view for displaying transnational transfer strategies for each tournament in the intercontinental football league and the country/region for the expert to browse, select and further analyze; an influence view, providing a customized visual component for an expert to observe the change of the bidirectional influence intensity and further analyze the causal relationship; and adjusting the view, providing an interaction method for an expert to adjust the quantity and quality of the nationwide meeting players and obtaining a simulation result. The method can analyze the influence intensity between the national transfer of football players and the national team performance in different countries, helps the expert to find the effect and influence of the football player transfer policy, and is convenient for the subsequent establishment of the appropriate football player national transfer policy.

Description

System and method for visualization of cross-border transfer analysis of football players

Technical field

The present invention relates to the technical field of data analysis and the technical field of visual analysis. Specifically, it relates to a system and method for visualizing football player transnational transfer analysis.

Background technique

Football is deeply loved by sports enthusiasts and is one of the most popular sports in the world. The cross-border transfer of professional football players has also become a phenomenon that cannot be ignored in the field of football. In recent years, the question of how players' cross-border transfers affect national team performance, and how national team performance affects players' cross-border transfers, has aroused great interest among researchers. The cross-border transfer of players represents the phenomenon of players playing in leagues outside their own country or region, which includes two directions of player flow, namely player import and player export. Specifically, most countries and regions in the world have organized domestic leagues, and each domestic league contains many clubs. Clubs are allowed to trade players from different countries and in the process facilitate two-way cross-border transfers of players.

In order to make appropriate decisions regarding player cross-border transfer policies, researchers have proposed many data-driven methods. Among them, statistical analysis, especially regression-based analysis, has been widely used to evaluate the impact between players' cross-border transfers and national team performance. However, the impact between players' cross-border transfers and national team performance is two-way, making analysis complex. Domain experts need causal relationships between bidirectional influences to fully analyze the influence process. However, existing regression-based solutions are limited to the unidirectional impact of cross-border transfers of players on national team performance, while ignoring causal relationships. This traditional regression model would hinder a full understanding of the impact of cross-border player transfers. At the same time, domain experts face challenges in deeply analyzing and interpreting the results of the above analytical models. In the case of developing cross-border transfer policies, domain experts need to quickly browse the raw data to select model inputs and interact with the model based on domain knowledge. Therefore, domain experts find it difficult to utilize statistical analysis methods in real-world applications to gain valuable insights that can be applied in decision-making scenarios.

Visual analytics technology enables expert interaction and interpretation to integrate domain knowledge into the analysis process to make model adjustments and explore detailed player information to improve understanding of results. However, in the field of sports visual analytics, most works mainly focus on detailed score table data and trajectory data for game result analysis and tactical analysis. There is still a lack of visual analytics methods tailored for cross-border transfers of players. At the same time, visualization work on regression models has paid little attention to the relationship between cross-border transfers and national team performance. Given the unique data structure and domain tasks, it is difficult to directly apply the current research to football transfer analysis.

Therefore, a visual method for cross-border transfer analysis of football players is needed to help domain experts explore the two-way impact between players' cross-border transfers and national team performance, and draw conclusions from a large amount of complex football player transfer data.

Contents of the invention

The purpose of the present invention is to provide a system and method for visualizing the cross-border transfer of football players, which can analyze the intensity of the impact between the cross-border transfer of football players from different countries and the performance of national teams, and help experts discover the consequences of football player transfer policies. The effect and impact will facilitate the subsequent formulation of appropriate cross-border player transfer policies.

In order to achieve the above object, in the first aspect, the system for analyzing and visualizing the cross-border transfer of football players provided by the present invention includes:

Country view, which displays cross-border transfer strategies for individual leagues in confederations and countries/regions for experts to browse, select and further analyze;

Impact View provides customized visualization components for experts to observe changes in bidirectional impact intensity and further analyze causal relationships;

Adjust the view to provide an interactive method for experts to adjust the quantity and quality of cross-border transfer players and obtain simulation results.

Preferably, the country view includes a map component for displaying an overview of global player cross-border transfers and a statistical world map, and a projected similarity component for providing global cross-border transfer strategies in terms of similarity.

The map component displays an overview of global player cross-border transfers and a statistical world map, allowing users to browse the continental football leagues of interest. Since cross-border transfer policies within the same continent are often similar, users typically select countries from a single continental football league (e.g. UEFA) for analysis, and select continental football leagues based on detailed cross-border transfer information. A statistical world map is provided in the map component to display the values of the cross-country transfer variables. Users can toggle the cross-country transfer variable using the button above the statistics world map. Opacity is used in the map component to encode specific values for variables that import players or export players to the selected country.

The similarity component provides a projection of global cross-border transfer strategies in terms of similarity. The user needs to analyze countries with similar cross-border transfer strategies. Therefore, a scatterplot is used in the similarity component to show the extent to which cross-border transfer strategies are similar across all countries. The positions of the points in the scatter plot are determined by the t-SNE projection algorithm. In the t-SNE projection, the original vector of a country consists of the total number of players who have transferred cross-border, the average market value, the average number of appearances and the average goals. These means are variables often used in the analysis of cross-border transfers of football players, usually Used to describe cross-border transfer strategies. By projecting the results, users can identify countries with similar cross-border transfer strategies. Opacity is used to code the cross-country transfer variable for each country in the scatterplot.

Among them, cross-border transfer variables include: number of players, player value, average number of appearances in a player's single season and average number of goals scored by a player in a single season.

Preferably, the country view includes the following interactive operations:

Time filtering, users can filter the time interval of interest by dragging the slider;

Direction switching, the user can use the button to switch whether to display imported players or exported players in the country view;

Country search, users search for countries of interest through the search bar;

For country selection, the user selects a continental football league in the map component by clicking or making a box selection, or selects a country in the similarity component, and the selected continental football league or country is highlighted in color.

Preferably, the impact view includes an impact diagram, which displays a bar chart and a timeline together to show the intensity of the impact over the years; the height of the bar chart represents the impact calculated by the cross-lag path analysis model. strength.

Preferably, in the detailed influence mode, use circles to encode the main direction of influence between player variables of cross-border transfers and national team performance. The color of the circle encodes the main direction of influence, while the area encodes the intensity of the influence in both directions. difference.

Influence diagrams can be used to visualize the results of a cross-lagged path analysis model. Comparing impact intensity by year is the main task in the impact view. So, to make a direct comparison, an impact diagram places a bar chart directly alongside a timeline to show the intensity of the impact over the years. The height of the bar graph represents the intensity of the influence calculated by the cross-lagged path analysis model (the higher the height, the greater the intensity of the influence). To clearly show changing trends in the intensity of the effects, in addition to linear scaling, an exponential scaling option for the bar height is available. The impact diagram can be switched between total impact and detailed impact. In the total impact mode, users can compare the total impact intensity of imported players and export players and discover changing trends at the same time. Circles are used in the detailed effects model to code the main direction of effects between player variables of cross-country transfers and national team performance. The color of the circle encodes the main direction of influence, while the area encodes the difference in intensity of influence in the two directions (the larger the area, the greater the difference in intensity of influence). Circles are aligned with corresponding bars in an influence diagram to facilitate the discovery of cause-and-effect relationships over time in both directions.

Preferably, the influence view includes the following interactive operations:

To merge the years, the user selects the year in the influence diagram, and the average value of each attribute in the selected year is used as the attribute of a single time point in the cross-lagged path analysis model;

To adjust variables, the user clicks the button in the upper left corner of the influence diagram to adjust the cross-country transfer variables involved in the calculation of the cross-lagged path analysis model;

To expand the comparison results, the user clicks the button on the left side of the impact diagram to expand the comparison results between different types of impacts;

Highlighting an attribute, the user clicks on a bar in the influence plot to highlight the bar at the same location each year for clear observation.

In the above influence view, you can select a year in the influence diagram. The average value of each attribute in the selected year will be used as an attribute at a single time point in the cross-lagged path analysis model; you can click on the upper left corner of the influence view. button to adjust the cross-country transfer variables involved in the calculation of the cross-lagged path analysis model; you can click the button on the left side of the impact diagram to expand the comparison results between different types of impacts (exports or imports); you can click in the impact diagram Bar chart to highlight the bars at the same location each year for clear observation.

Preferably, the adjustment view includes an adjustment part and a result part, the adjustment part includes a slider corresponding to the selected cross-border transfer variable in the influence view, and the user adjusts the cross-border transfer through the slider. Player variable; the results section contains text representing the exact value of the change in the national team's performance.

Users can adjust player variables for cross-border transfers through the slider in the adjustment section and view the adjustment results in real time. Two adjustment panels are placed in the adjustment view by target year and arranged in chronological order to allow comparison between two-way cross-border transfer adjustments in adjacent years. In this way, the user can compare the results section of the adjustment panel and draw conclusions as to which year and in which direction adjustments could significantly improve the performance of the national team.

The views in the above visual analysis system are related and provide a rich interaction mechanism, including click, swipe selection, switching, frame selection, mouse hovering and dragging the slider bar.

For click actions, in the countries view, the user can click to select confederations in the map component or to select a country in the similarity component. In the Impact view, users can adjust the cross-country transfer variables that are calculated in the cross-lagged path analysis model by clicking the button in the upper left corner of the Impact view, and by clicking on the bars in the Impact plot to highlight the cross-country transfer variables at the same location each year. Bar graph for clear observation.

For the brush selection operation, in the influence view, the user can brush the year in the influence diagram, and the average value of each attribute in the brush-selected year will be used as the attribute of a single time point in the cross-lagged path analysis model.

For switching operations, in the country view, the user can switch whether to display imported players or export players in the country view through the button. In the Impact view, the user can click the button to the left of the Impact graph to expand the comparison results between impacts of different types (export or import).

For the box selection operation, in the country view, the user can select a country in the similarity component through mouse box selection, or can reversely select and remove an already selected country through mouse box selection.

For mouseover operations, in the country view, users can obtain detailed information on cross-border transfer variables of players in the Confederations and Countries of the Map component and the Similarity component via mouseover. In the adjustment view, users can see the exact value of the cross-country transfer variable for that country by hovering the mouse over the slider in the adjustment section.

For dragging the slider bar operation, in the country view, the user can filter the time interval of interest by dragging the slider above the view. In the Adjustments view, users can adjust player variables for cross-country transfers in the Adjustments section by dragging the sliders.

In the second aspect, the method for visualizing the analysis and visualization of the cross-border transfer of football players provided by the present invention is based on the system implementation of the above-mentioned visualization and analysis of the transnational transfer of football players, and includes the following steps:

1) Obtain the cross-border transfer data of players in the top football leagues of each country, that is, the number of players, the player's worth, the average number of players' appearances in a single season and the average number of goals scored by the players in a single season, and obtain the FIFA ranking points of each country;

2) Visualize the cross-border transfer data of football players obtained in step 1) to obtain an overview of the cross-border transfer variables of global football players, obtain the dimensionality reduction projection results of the cross-border transfer variables of players from each country, and select a country. identify data sets for further analysis;

3) Use the cross-lagged path analysis model to calculate the impact intensity based on the country selected in step 2), visualize the impact intensity and display it in the impact view, and conduct a detailed analysis of the impact between player cross-border transfers and national team performance; at the same time, select the year , adjust the selected year in the adjustment view;

4) Based on the country selected in step 2) and the year selected in step 3), adjust the cross-border transfer variables in the adjustment view and obtain the results of the adjusted national team performance.

Step 2) includes:

2-1) Drag the slider bar to select the cross-border transfer time period for the players you are interested in;

2-2) Click the buttons corresponding to the four cross-border transfer variables to switch the variables of interest;

2-3) Click the buttons corresponding to player input and player output to switch the direction of player cross-border transfer;

2-4) Add the interested continental football leagues or countries to the impact view by clicking or selecting them with the mouse, and remove the selected countries by reverse selection with the mouse.

Step 3) includes:

3-1) Click on the player's cross-border transfer attribute to select the attribute used to calculate the impact intensity in the cross-lagged path analysis model;

3-2) Observe the total impact of input players and output players on the performance of the national team and the comparison results;

3-3) Click the Import Player or Export Player button to view the detailed impact and causal relationship between the input player or export player and the performance of the national team;

3-4) Click the year label to select the year, and adjust the selected year in the adjustment view.

Compared with the prior art, the benefits of the present invention are:

In the method for visualizing football player transnational transfer analysis provided by the present invention, football data analysts only need to import football player transnational transfer data and national team performance data in corresponding formats, and can easily, intuitively, comprehensively and in-depth analyze football players. Conduct effective analysis of athletes' cross-border transfer policies to quickly and clearly explore the intensity of the impact between football players' cross-border transfers and national team performance, so that subsequent more targeted adjustments to football players' cross-border transfer policies can be made. Experts in the field have given high recognition to this invention.

Description of drawings

Figure 1 is a schematic interface diagram of a system for analyzing and visualizing the cross-border transfer of football players in an embodiment of the present invention;

Figure 2 is a schematic diagram of an influence diagram in an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described below with reference to the embodiments and the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the described embodiments, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Unless otherwise defined, technical terms or scientific terms used in the present invention shall have the usual meaning understood by a person with ordinary skill in the field to which the present invention belongs. Similar words such as "include" or "include" used in the present invention mean that the elements or things appearing before the word include the elements or things listed after the word and their equivalents, without excluding other elements or things. Words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to express relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

Example

Referring to Figure 1, the system for analyzing and visualizing the cross-border transfer of football players in this embodiment includes:

The country view, which contains a map component (shown as A1 in Figure 1) and a similarity component (shown as A2 in Figure 1), displays the cross-border transfer strategies of various leagues in continental football leagues and countries/regions to facilitate experts. Browse, select and further analyze;

Impact view provides a customized visual component impact diagram (shown as B in Figure 1), including total impact (shown as B2 in Figure 1) and detailed impact (shown as B3 in Figure 1), and experts can observe the two-way impact Changes in intensity and further analyze causal relationships;

The adjustment view (shown as C in Figure 1) provides an interactive method so that experts can adjust the quantity and quality of cross-border transfer players and obtain simulation results.

Football analysts involved in the assessment need to observe the impact of the cross-border player transfer policy in the Union of European Football Associations (UEFA) and provide reference for further policy formulation. According to analysts, the top leagues among UEFA countries, especially the top five, are the most developed, attracting thousands of top players from around the world. UEFA plays an important role in the global trend of international player transfers. Analysts are therefore interested in cross-border transfers of players from UEFA countries.

The map component in the country view displays an overview of global player cross-border transfers as well as a statistical world map to allow analysts to browse the continental football leagues of interest (shown as A1 in Figure 1). Analysts can toggle the cross-country transfer variable using the button above the statistics world map. Opacity is used in the map component to encode specific values for variables that import or export players to the selected country. The similarity component uses a scatter plot to display the dimensionality reduction projection results of cross-border transfer variables for all countries (shown in A2 in Figure 1). Opacity is used to code the cross-country transfer variable for each country in the scatterplot. Selected countries are highlighted with color in both the Map component and the Similarity component, allowing analysts to observe selected confederations or countries in both components at the same time. Move the slider over the view to filter the time period of interest. At the same time, you can use the switch button to switch whether to display imported players or exported players in the overview component. Analysts can click on the confederation of interest in the map component, or select the country of interest in the similarity component for further detailed analysis.

First, the analysts adjusted the time slider to 1992 to 2018 and selected all UEFA countries in the country view (shown as A1 in Figure 1). The analysts then looked at the values of the imported player variable and the exported player variable averaged by year in the map component. Through observation, the analysts found that UEFA countries had significantly higher values for each of the imported player variables than other countries, and also had higher values for the exported player variables than most other countries. Therefore, the analysts focused on the impact between player imports and UEFA national team performance in further analysis, and added all UEFA countries/regions into the impact view.

The Impact View contains a newly designed impact diagram to visualize the results of the cross-lagged path analysis model (shown as B2, B3 in Figure 1). Comparing impact intensity by year is the main task in the impact view. Therefore, to make a direct comparison, the system places a bar chart directly alongside a timeline to show the intensity of the impact over the years. The height of the bar represents the strength of the effect calculated by the cross-lagged path analysis model. To clearly show changing trends in the intensity of the effects, in addition to linear scaling, an exponential scaling option for the bar height is available. The impact diagram can be switched between total impact and detailed impact. In the total impact mode, analysts can compare the total impact strength of imported players and export players and discover changing trends at the same time. In detailed impact mode, the system uses circles to encode the main direction of impact between player variables of cross-country transfers and national team performance. The color of the circle encodes the main direction of influence, while the area encodes the difference in intensity of influence in the two directions.

For example, use the impact diagram in the detailed impact model to illustrate the analysis of exported players from 2015 to 2018, as shown in Figure 2. The upper set of bars represents the impact of exported players on national team performance, and the lower set represents the impact of national team performance on exported players. Each column of the annual impact graph contains four bars. The four bar graphs respectively represent the effects related to the number of players, market value, number of appearances and goals (shown as B, D in Figure 2). When analysts focus on trends in the strength of the effect between a specific cross-country transfer variable and national team performance, they can compare bar charts for the same position each year. To discover the main direction of influence between export players and national team performance, analysts can observe the circle between the two bars (shown as C in Figure 2). Through various comparisons, analysts can conduct a comprehensive analysis of players' cross-border transfers.

After adding UEFA countries/regions to the impact view, analysts began to evaluate the effects of policies on important cross-border transfers proposed between 1992 and 2018 (shown in Figure 1, B). Analysts pointed out that players’ cross-border transfer policies directly affect the number and market value of cross-border transfer players. Therefore, the analysts selected these two cross-country transfer variables in the analysis below. Given the delayed nature of policy effects, analysts combine three or four consecutive years to more clearly observe its impact. The results are displayed in the Impact view as a bar chart.

Regarding the total impact (shown as B2 in Figure 1), the analysts found that the total impact from imported players to national team performance gradually increased from 1996 to 2010, decreased slightly starting in 2011, and remained constant after 2011 Stablize. This is consistent with the analysts' knowledge. Analysts explain that this is due to the impact of the Bosman Act of 1995 and the introduction of the Homegrown Player Rule in 2006. According to analysts, the Bosman Act is intended to allow players to move more freely between European clubs and facilitate cross-border transfers of players between UEFA countries. UEFA proposed the "Home Player Rule" to enforce the number of local players participating in competitions and further adversely affected cross-border transfers of players from other countries to UEFA countries. Analysts concluded that the Bosman Act significantly increased the impact of imported players on the national team's performance, while the Homegrown Players Rule reduced the intensity of this impact to a certain extent. Therefore, analysts analyze the internal processes of impact by observing the impact in detail.

For the detailed impact (shown as B3 in Figure 1), analysts observe the two selected cross-border transfer variables separately. To assess the impact of player numbers, analysts found that from 1996 to 1998, the number of imported players was significantly affected by the performance of the national team, consistent with the effect of the Bosman Act. Analysts explained that in a free transfer environment, foreign players are more likely to choose countries with a higher level of national teams to seek better development opportunities. Later, analysts noticed that from 1999 to 2007, the impact of imported players on the performance of the national team greatly exceeded other directions of influence. They explained that following the large-scale cross-border player transfers, the import of players from UEFA countries has had a positive impact on the level of domestic players and further affected the performance of the national team. At the same time, with the cross-border transfer of players during this period, the number of imported players from other UEFA countries also gradually increased, which reduced the impact of the national team's performance on the number of imported players. The analysts also explained that the change in the intensity of the impact after 2008 corresponds to the impact of the Local Players Rule, which resulted in a decrease in the impact of national team performance on the number of imported players and an increase in the impact in the other direction. To assess the impact of market value, analysts observed that from 2008 to 2010, the impact of imported players on national team performance was very large. This shows that players were overpriced during this period, which was effectively controlled by the UEFA Financial Fair Play Regulations proposed in 2010.

After selecting the target year in the impact view, analysts can conduct further simulation analysis in the adjustment view (shown as C in Figure 1). Analysts need to conduct simulation analysis based on the cross-lagged path analysis model. Analysts tend to adjust the quantity and quality of imported and exported players and then evaluate how the national team's performance changes. Therefore, the adjustment panel is provided in the impact view for analysts to adjust cross-border transfer variables and directly analyze the results (shown as C1 and C2 in Figure 1). The system focuses on adjusting operations and displaying results rather than demonstrating the simulation process. The adjustment panel can be divided into two parts, the adjustment part and the results part. The Adjustments section contains sliders corresponding to the cross-country transfer variables selected in the Impact view. The results section contains some text to represent the exact value of the change in the national team's performance. Analysts can adjust player variables for cross-border transfers in the adjustment section via sliders and view the adjustment results in real time. Place two adjustment panels by target year in the adjustment view and arrange them in chronological order to allow comparison between adjustments for two-way cross-country transfers in adjacent years. In this way, analysts can compare the resulting sections of the adjustment panel and draw conclusions as to which year and in which direction adjustments were made that significantly improved the national team's performance.

In the adjustment view, the analysts selected Argentina, a typical export-dominated country, to test their hypothesis (shown as C in Figure 1). Analysts compared Argentina's imported players and exported players and found that the quantity and quality of exported players exceeded that of imported players, which proves that Argentina is a country dominated by exports. The analysts then slightly increased the number of exported players and noted that Argentina's FIFA points and rankings also improved. Therefore, experts in the field confirm that increasing the number of exported players can improve national team performance in countries where player exports dominate.

In summary, this method of visualizing football players' cross-border transfer analysis can effectively and quickly help users explore the relationship between football players' cross-border transfers and national team performance, and supports powerful multi-step analysis.

Claims (9)

1. A system for cross country transfer analysis visualization for football players, comprising:

a national view for displaying transnational transfer strategies for each tournament in the intercontinental football league and the country/region for the expert to browse, select and further analyze;

an influence view, providing a customized visual component for an expert to observe the change of the bidirectional influence intensity and further analyze the causal relationship;

adjusting views, providing an interaction method for an expert to adjust the quantity and quality of the nationwide meeting players and obtaining a simulation result;

the interaction between views in the system comprises the following steps:

1) Acquiring the cross-country transfer data of players in the top-level football tournament of each country, namely the number of players, the price of the players, the average number of times of play of the single season of the players and the average number of goals of the single season of the players, and acquiring the international football rank points of each country;

2) Visualizing the nationwide transfer data of the football players obtained in the step 1) to obtain an overview of the nationwide transfer variables of the global football players, obtaining a dimension reduction projection result of the nationwide transfer variables of the football players in each country, selecting one country, and determining a data set for further analysis;

3) Calculating influence intensity by adopting a cross hysteresis path analysis model according to the country selected in the step 2), visualizing the influence intensity and displaying the influence intensity to an influence view, and carrying out detailed analysis on influence between the transition of the player across the country and the performance of the team of the country; simultaneously selecting the year, and adjusting the selected year in the adjustment view;

4) And according to the country selected in the step 2) and the year selected in the step 3), adjusting the transnational transfer variables in the adjustment view and obtaining the result of the adjusted national team performance.

2. The system for visualizing a national transition analysis for a soccer player of claim 1, wherein said national view comprises a map component for displaying a global player national transition overview and a statistical world map and a projected similarity component for providing a global national transition strategy in terms of similarity.

3. The system for cross-country transfer analysis visualization of football players of claim 2, wherein the country view comprises the following interactions:

time screening, wherein a user filters an interested time interval by dragging a sliding block;

a direction switch, wherein the user displays an import player or an export player in the national view through button switch;

country searching, wherein a user searches for a country of interest through a search bar;

the user selects an intercontinental football league in the map component or a country in the similarity component by clicking or framing the selected intercontinental football league or country to be highlighted in color.

4. The system for cross-country transfer analysis visualization of soccer player of claim 1, wherein the impact view comprises an impact graph that places a bar graph with a timeline to show impact strength over years; the height of the bar graph represents the impact strength calculated by the cross-hysteresis path analysis model.

5. The system for cross-country turn analysis visualization of football players of claim 4, wherein in the detailed influence mode, circles are used to encode the dominant directions of influence between player variables and the national team performance of the cross-country turn, the colors of the circles encode the dominant directions of influence, and the areas encode the differences in the intensities of influence of the two directions.

6. The system for cross-country transfer analysis visualization of soccer player of claim 4, wherein the influence view comprises the following interactions:

merging years, brushing the years in an influence graph by a user, wherein the average value of all the attributes of the brushed years is taken as the attribute of a single time point in the cross hysteresis path analysis model;

adjusting variables, wherein a user adjusts the transnational transfer variables participating in calculation in the cross hysteresis path analysis model by clicking a button at the upper left corner of the influence diagram;

expanding the comparison result, wherein a user clicks a button on the left side of the influence graph to expand the comparison result between different types of influences;

highlighting attributes the user clicks on the bar graph in the influence graph to highlight the bar graph at the same location each year for clear observation.

7. The system for visualizing a national transition analysis for a soccer player according to claim 1, wherein said adjustment view comprises an adjustment portion and a result portion, said adjustment portion comprising a slider corresponding to a selected national transition variable in said influence view, whereby a user adjusts a player variable of a national transition; the results section contains text representing the exact value of the national team performance change.

8. The system for cross country transfer analysis visualization of a soccer player of claim 1, wherein step 2) comprises:

2-1) dragging a sliding bar to select a cross-country transfer time period of the interested player;

2-2) clicking buttons corresponding to four transnational meeting variables to switch the variables of interest;

2-3) clicking buttons corresponding to player input and player output to switch the direction of the player transferring across the country;

2-4) adding the intercontinental football league or country of interest to the influence view by clicking or mouse-over, and removing the already selected country by mouse-over counter-selection.

9. The system for cross country transfer analysis visualization of a soccer player of claim 1, wherein step 3) comprises:

3-1) clicking a cross hysteresis path analysis model selected by a player transnational transfer attribute to calculate an attribute adopted by the influence intensity;

3-2) observing the total influence intensity of the input player and the output player on the national team performance and comparing the result;

3-3) clicking an input player or output player button to check detailed influences and causal relations between the input player or output player and the national team performance;

3-4) clicking on the year tab selection year, and adjusting the selected year in the adjustment view.